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Compactness and Separateness Driven Fuzzy Clustering Validity Index Called TLW

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2013))

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Abstract

The design of validity index of fuzzy clustering has always been a historical problem in fuzzy clustering field. When the distribution of cluster centers is very close, it is difficult for the existing fuzzy clustering validity indexes to obtain a reasonable cluster number, and the separation mechanism of these indexes is too simple. In order to solve the above problems, we propose a novel fuzzy clustering validity index called TLW (Tang-Li-Wang) index. Firstly, compactness is expressed as the ratio of the membership weighted distance value to the sample variance of the dataset. Secondly, the sum of the maximum distance between cluster centers and the mean distance is used in separateness, and the sample variance of cluster centers is introduced, and the two are multiplied to describe the separateness. Thirdly, on the basis of considering compactness and separateness, the introduction of cluster number can alleviate the phenomenon that the index value may change monotonically with the increase of cluster number. Finally, the classical FCM (Fuzzy C-Mean) algorithm is used to conduct experiments on indexes. Comparative experiments and analyses were carried out on 17 typical datasets and 12 clustering validity indexes. From the experimental results of normal simple datasets and high-dimensional difficult datasets, the proposed index shows some advantages. All in all, these results verify that the proposed TLW index has better accuracy and stronger stability.

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Acknowledgment

It was subsidized from National Natural Science Foundation of China (62176083, 62176084) and Fundamental Research Funds for Central Universities of China (PA2023GDSK0061).

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Authors and Affiliations

  1. Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, Hefei University of Technology, Hefei, 230601, Anhui, China

    Yiming Tang, Xiang Wang, Bing Li, Xianghui Hu & Wenjun Xie

  2. Engineering Research Center of Safety Critical Industry Measure and Control Technology, Ministry of Education, Hefei University of Technology, Hefei, 230601, Anhui, China

    Yiming Tang

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  1. Yiming Tang
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  2. Xiang Wang
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  3. Bing Li
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  4. Xianghui Hu
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  5. Wenjun Xie
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Corresponding author

Correspondence to Yiming Tang .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Harbin Engineering University, Harbin, China

    Tong Wang

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. Tongji University, Shanghai, China

    Bowen Du

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Tang, Y., Wang, X., Li, B., Hu, X., Xie, W. (2024). Compactness and Separateness Driven Fuzzy Clustering Validity Index Called TLW. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_13

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  • DOI: https://doi.org/10.1007/978-981-99-9640-7_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9639-1

  • Online ISBN: 978-981-99-9640-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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